The employee and customer worlds are colliding, and it's time to stop sitting back and watching. The time has come to act before another day of opportunity is missed -- and before heads roll in HR.

Here's the rub. At every HR conference I, Jeremy, have attended this year -- and there have been many -- attendees were hit with the idea that "it's time to treat your employees like customers!" Thanks conference organizers for allowing presenters to hammer that point home -- repeatedly. Unfortunately, HR staff hasn't had to treat customers like customers. Well, that is, if you aren't including that college job when they waited on patrons at the local Houlihan's.

Essentially HR is being asked to do the impossible and solve something it hasn't been equipped to solve. Nothing new there, right? That's when it hit me. Customer service is a concept so core to business that it has been honed over centuries -- or since the first company opened its doors. In recent years it has gone through an incredible renaissance with the application of insights gained from the ever-so-popular discipline of big data. So let's fast forward through the centuries of trial and error experienced on the customer side and arrive at what many call the holy grail -- big data analytics. Not only that, but let's apply the techniques and examples that have been learned on the customer side of the equation to get HR there.

I'm no stranger to client service by virtue of my work servicing my Hive Tech clients, as well as my previous consulting and implementation work. And yes, I'm that Houlihan's waiter. However, I don't know know the customer side well enough to handle both sides of the equation, especially as it relates to big data.

Enter coauthor Sahil Kedar, who works on the customer 360-degree view, or customer 360 (C360), which is all about analyzing the touch points and demographic information of your customer base to better understand what's happening with your company and its products. In this column, Sahil explains the basics of the customer 360-degree view and provides case studies and insights that provide real-life context. Along the way, I'll include comparisons to the world of employees, which I'll call E360.

Basics of C360

C360 typically originates in sales and marketing. Let's start with the "omnichannel conversion analysis." That's a fancy way of referring to getting data from multiple sources. There are three key areas of the customer 360-degree view:

Deep consumer insights to understand the path to purchase and influences to purchase;

A technology backbone, including data analytics, to develop predictive models for effective consumer engagement; and

Engagement across functions and the entire leadership -- not just marketing and sales.

As with customers, one simple employee questionnaire does not provide a 360-degree view of them. To dive into customer insights, we first need to do some audience segmentation. This is a process of dividing people into homogeneous subgroups based upon defined criteria such as product usage, demographics, psychographics, communication behaviors and media use.

For E360, this means that you need to segment based on criteria such as age and marital status. After all, young single employees will have different agendas for their career longevity and use of free time than older married employees. One size does not fit all in these cases. It may be scary to do this, but you're not making hiring or firing decisions based on this segmentation and it isn't at an individual level.

From there we move on to behavior analysis. This is the process of systematically applying interventions based upon the principles of learning theory to improve socially significant behaviors to a meaningful degree, and demonstrating that the interventions were the cause of the the improvement. When we do our segmentation and then evaluate behaviors, we look at whether we are seeing the expected or appropriate outcome. If the results are unexpected, we need to look at why and determine the next steps. From an E360 standpoint this is like starting a recognition program and evaluating the results.

C360 automobile resale case study

A large company that focuses on automobile resale needed a way to determine the types of behavior that lead to a customer buying a car. It wanted to look at whether customers performed extensive online research or lots of research at the physical dealership. The company also wondered whether it could track someone from online to the dealer lot. Although these are common questions for such a company, it also wanted to decrease investments in traditional enterprise data warehouses and analytic databases.

The company decided to use a business analytics tool that analyzes data in Hadoop (open source software that enables distributed processing of large data sets). The company integrated and visualized TV spot data, website traffic metrics and car dealer inventory to better understand the influence on the consumer's journey. The company created dashboards that demonstrated immediate consumer behavior shifts influenced by OEM TV ads during the Super Bowl. The project will achieve a positive ROI in six weeks, which is significantly less time and money than if the company used legacy tools.

The best comparison for E360 might be to swap the car buyer with a candidate for employment. Sure, candidates aren't yet employees, but if all goes well, they soon will be. What if you could track somebody from the job board to the interview room? What if you could better understand the influence on the candidate's journey?

C360 Affordable Care Act case study

The government is reviewing the effectiveness of the Affordable Care Act (ACA) honing in on the applicants as the ACA's customers. Data administrators are attempting to answer a number of questions. For example:

Applicants start to enroll online and then bail out before they finish. Why do they bail?

Some applicants who bailed will come back later (maybe weeks or months later) and either reapply or finish their application. What made them come back? Was it a personal choice or did the ACA offer a new feature they were attracted to?

What demographic is most likely to apply for ACA? What are the qualities of these segments that we need to concentrate on or do more research on?

Can we find the applicants' differences based on state of origin or income levels? Which applicants are using applied tax credits?

Bringing in the concept of omnichannels, can the government track behavior across site viewing habits? For instance, if someone goes from the ACA site to the Blue Cross Blue Shield site, will the government lose them? In those cases, can we conclude that the citizens found better coverage? Or do they come back and therefore, ACA is the better choice? This has major marketing implications.

The ACA case study compares well with the employee experience when enrolling in benefits, including new hires, open enrollment and life events. That experience matters to employees, and it can be a major source of dissatisfaction if not executed correctly. If they bail out of enrollment before they finish, why? And looking at benefits from the employer's perspective, can we determine what demographic groups are more likely to use benefits? And if so, can we trace how that affects a company's bottom line?

Although we've only cracked the surface, you can see that by using the concepts of a customer 360-degree view, you can find interesting applications for analyzing employees as customers. We still haven't treated employees like customers, but you can't develop programs for treating them like customers until you fully understand them. Once you have that understanding, you can do the behavior analysis as discussed.

In the second article in this series, we'll look at some E360 case studies and their application to C360.

About the authorsJeremy Ames is co-founder of Hive Tech HR, a consulting company based in Medway, Mass. He is a 15-year veteran of IT implementation projects and sits on the HR Management and Technology Expertise panel of the Society for Human Resource Management.

Sahil Kedar is a senior-level solutions engineer who has worked in big data, data warehousing and analytics for more than 15 years. Working at companies such as IBM as well as San Francisco startups, he has engineered and installed solutions for customers including Bank of America, American Express and Fannie Mae.

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I'd think about improving our customer interactions long before foisting an almost-broken system on our employees. "We appreciate your work here but all managers are currently busy. Current wait time will be....